BE Study Sample Size Calculator

Bioequivalence Study Sample Size Formula:

\[ n = \frac{Z^2 \times (p_1(1-p_1) + p_2(1-p_2))}{(p_1 - p_2)^2} \times 2 \]

Sample Size per Group (n):

Unit Converter ▲

Unit Converter ▼

From:	To:

1. What is Bioequivalence Study Sample Size Calculation?

Bioequivalence study sample size calculation determines the number of participants needed per group to demonstrate that two formulations (test and reference) are equivalent in terms of rate and extent of absorption.

2. How Does the Calculator Work?

The calculator uses the bioequivalence sample size formula:

\[ n = \frac{Z^2 \times (p_1(1-p_1) + p_2(1-p_2))}{(p_1 - p_2)^2} \times 2 \]

Where:

\( n \) — Sample size per group
\( Z \) — Z-value (typically 1.96 for 90% confidence)
\( p_1 \) — Proportion for group 1 (decimal)
\( p_2 \) — Proportion for group 2 (decimal)
\( p_1 - p_2 \) — Difference between proportions (delta)

Explanation: This formula calculates the required sample size per group to achieve adequate statistical power for detecting specified differences in proportions between two groups.

3. Importance of Sample Size Calculation

Details: Proper sample size calculation ensures studies have sufficient power to detect meaningful differences, prevents underpowered studies, and optimizes resource allocation while maintaining statistical validity.

4. Using the Calculator

Tips: Enter proportions as decimals (e.g., 0.8 for 80%), use Z-value of 1.96 for 90% confidence. Ensure proportions are between 0 and 1, and that they are not equal.

5. Frequently Asked Questions (FAQ)

Q1: Why is Z-value typically 1.96?
A: Z-value of 1.96 corresponds to 90% confidence level, which is standard for bioequivalence studies requiring 80-125% equivalence margins.

Q2: What are typical proportion values in BE studies?
A: Proportions typically range from 0.8 to 0.95 depending on the expected bioavailability and variability of the drug product.

Q3: When should this calculation be used?
A: During study planning phase to determine required participant numbers for adequate statistical power in crossover or parallel design studies.

Q4: Are there limitations to this calculation?
A: This assumes normal distribution and equal variances. For highly variable drugs or non-normal distributions, more complex methods may be needed.

Q5: How does this relate to regulatory requirements?
A: Regulatory agencies require adequate sample size justification in study protocols to ensure valid bioequivalence conclusions.